Depuydt Emma, Criel Yana, De Letter Miet, van Mierlo Pieter
Medical Imaging and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium.
BrainComm Research Group, Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium.
Front Neurosci. 2024 Oct 8;18:1443752. doi: 10.3389/fnins.2024.1443752. eCollection 2024.
Event-Related Potentials (ERPs) are valuable for studying brain activity with millisecond-level temporal resolution. While the temporal resolution of this technique is excellent, the spatial resolution is limited. Source localization aims to identify the brain regions generating the EEG data, thus increasing the spatial resolution, but its accuracy depends heavily on the head model used. This study compares the performance of subject-specific and template-based head models in both simulated and real-world ERP localization tasks.
Simulated data mimicking realistic ERPs was created to evaluate the impact of head model choice systematically, after which subject-specific and template-based head models were used for the reconstruction of the data. The different modeling approaches were also applied to a face recognition dataset.
The results indicate that the template models capture the simulated activity less accurately, producing more spurious sources and identifying less true sources correctly. Furthermore, the results show that while creating more accurate and detailed head models is beneficial for the localization accuracy when using subject-specific head models, this is less the case for template head models. The main N170 source of the face recognition dataset was correctly localized to the fusiform gyrus, a known face processing area, using the subject-specific models. Apart from the fusiform gyrus, the template models also reconstructed several other sources, illustrating the localization inaccuracies.
While template models allow researchers to investigate the neural generators of ERP components when no subject-specific MRIs are available, it could lead to misinterpretations. Therefore, it is important to consider a priori knowledge and hypotheses when interpreting results obtained with template head models, acknowledging potential localization errors.
事件相关电位（ERP）对于以毫秒级时间分辨率研究大脑活动具有重要价值。虽然该技术的时间分辨率极佳，但其空间分辨率有限。源定位旨在识别产生脑电图数据的脑区，从而提高空间分辨率，但其准确性在很大程度上取决于所使用的头部模型。本研究比较了特定受试者头部模型和基于模板的头部模型在模拟和实际ERP定位任务中的性能。
创建模拟逼真ERP的模拟数据，以系统评估头部模型选择的影响，之后使用特定受试者头部模型和基于模板的头部模型对数据进行重建。不同的建模方法也应用于一个人脸识别数据集。
结果表明，模板模型对模拟活动的捕捉不够准确，产生了更多虚假源，正确识别的真实源较少。此外，结果表明，虽然创建更准确、更详细的头部模型在使用特定受试者头部模型时有利于提高定位准确性，但对于模板头部模型而言情况并非如此。使用特定受试者模型时，人脸识别数据集的主要N170源被正确定位到梭状回，这是一个已知的面部处理区域。除了梭状回，模板模型还重建了其他几个源，说明了定位不准确的情况。
虽然当没有特定受试者的磁共振成像（MRI）时，模板模型允许研究人员研究ERP成分的神经发生器，但这可能会导致误解。因此，在解释使用模板头部模型获得的结果时，考虑先验知识和假设并认识到潜在的定位误差非常重要。
